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attention Markets of Blockchain-Based Decentralized Autonomous Organizations

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1370-1380页

作者： Juanjuan Li Rui Qin Sangtian Guan Wenwen Ding Fei Lin Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

The attention is a scarce resource in decentralized autonomous organizations(DAOs),as their self-governance relies heavily on the attention-intensive decision-making process of“proposal and voting”.To prevent the negative effects of pro-posers’attention-capturing strategies that contribute to the“tragedy of the commons”and ensure an efficient distribution of attention among multiple proposals,it is necessary to establish a market-driven allocation scheme for DAOs’attention.First,the Harberger tax-based attention markets are designed to facilitate its allocation via continuous and automated trading,where the individualized Harberger tax rate(HTR)determined by the pro-posers’reputation is adopted.Then,the Stackelberg game model is formulated in these markets,casting attention to owners in the role of leaders and other competitive proposers as followers.Its equilibrium trading strategies are also discussed to unravel the intricate dynamics of attention pricing.Moreover,utilizing the single-round Stackelberg game as an illustrative example,the existence of Nash equilibrium trading strategies is demonstrated.Finally,the impact of individualized HTR on trading strategies is investigated,and results suggest that it has a negative correlation with leaders’self-accessed prices and ownership duration,but its effect on their revenues varies under different conditions.This study is expected to provide valuable insights into leveraging attention resources to improve DAOs’governance and decision-making process.

关键词： attention decentralized autonomous organizations Harberger tax Stackelberg game.

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Adaptation of Ocular Opponency Neurons Mediates attention-Induced Ocular Dominance Plasticity

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Neuroscience Bulletin 2024年第3期40卷 339-349页

作者： Fangxing Song Lili Lyu Min Bao CAS Key Laboratory of Behavioral Science Institute of PsychologyChinese Academy of SciencesBeijing100101China Department of Psychology University of Chinese Academy of SciencesBeijing100049China Institute of Neuroscience Key Laboratory of Primate NeurobiologyCAS Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China

Previous research has shown that ocular dominance can be biased by prolonged attention to one eye.The ocular-opponency-neuron model of binocular rivalry has been proposed as a candidate account for this phenomenon.Yet direct neural evidence is still lacking.By manipulating the contrast of dichoptic testing gratings,here we measured the steady-state visually evoked potentials(SSVEPs)at the intermodulation frequencies to selectively track the activities of ocular-opponency-neurons before and after the“dichoptic-backward-movie”adaptation.One hour of adaptation caused a shift of perceptual and neural ocular dominance towards the unattended eye.More importantly,we found a decrease in the intermodulation SSVEP response after adaptation,which was significantly greater when high-contrast gratings were presented to the attended eye than when they were presented to the unattended eye.These results strongly support the view that the adaptation of ocular-opponency-neurons contributes to the ocular dominance plasticity induced by prolonged eye-based attention.

关键词： attention Ocular dominance Opponency neuron Adaptation Steady-state visually evoked potential

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Clean air captures attention whereas pollution distracts:evidence from brain activities

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Frontiers of Environmental Science & Engineering 2024年第4期18卷 5-15页

作者： Jianxun Yang Yunqi Liu Berry van den Berg Susie Wang Lele Chen Miaomiao Liu Jun Bi State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210023China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing 210044China Nanjing Foreign Language School Nanjing 210008China Department of Experimental Psychology University of GroningenGrote Kruisstraat 2/19712 TSGroningenThe Netherlands Department of Social Psychology University of GroningenGrote Kruisstraat 2/19712 TSGroningenThe Netherlands School of Education Science Nantong UniversityNantong 226019China

Awareness of the adverse impact of air pollution on attention-related performance such as learning and driving is rapidly growing.However,there is still little known about the underlying neurocognitive mechanisms.Using an adapted dot-probe task paradigm and event-related potential(ERP)technique,we investigated how visual stimuli of air pollution influence the attentional allocation process.Participants were required to make responses to the onset of a target presented at the left or right visual field.The probable location of the target was forewarned by a cue(pollution or clean air images),appearing at either the target location(attention-holding trials)or the opposite location(attention-shifting trials).Behavioral measures showed that when cued by pollution images,subjects had higher response accuracy in attention-shifting trials.ERP analysis results revealed that after the cue onset,pollution images evoked lower N300 amplitudes,indicating less attentioncapturing effects of dirty air.After the target onset,pollution cues were correlated with the higher P300 amplitudes in attention-holding trials but lower amplitudes in attention-shifting trials.It indicates that after visual exposure to air pollution,people need more neurocognitive resources to maintain attention but less effort to shift attention away.The findings provide the first neuroscientific evidence for the distracting effect of air pollution.We conclude with several practical implications and suggest the ERP technique as a promising tool to understand human responses to environmental stressors.

关键词： Air pollution attention Disengagement Performance Event-related potential

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Workout Action Recognition in Video Streams Using an attention Driven Residual DC-GRU Network

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Computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmo

关键词： Workout action recognition video stream action recognition residual network GRU attention

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基于CNN-LSTM-attention和自回归的混合水位预测模型(英文)

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水利水电技术(中英文) 2024年

作者：吕海峰涂井先林泓全冀肖榆梧州学院广西机器视觉与智能控制重点实验室

【目的】水位预测对交通运输、农业以及防洪措施具有重要影响。精确的水位值可用于提升水道运输的安全及效率、降低洪水风险，同时也是保障区域可持续发展的必要条件。【方法】提出一种CRANet的混合水位预测模型，以卷积神经网络(CNN)... 详细信息

【目的】水位预测对交通运输、农业以及防洪措施具有重要影响。精确的水位值可用于提升水道运输的安全及效率、降低洪水风险，同时也是保障区域可持续发展的必要条件。【方法】提出一种CRANet的混合水位预测模型，以卷积神经网络(CNN)、长短期记忆网络(LSTM)、注意力机制以及自回归(AR)组件为基础，旨在应对时间序列数据中存在的线性与非线性问题，缓解自回归及ARIMA模型的缺陷。其应用不仅在于为航运调度提供决策支撑，加强导航安全效率，同样能提升防洪减灾的能力。其中，CNN和LSTM组件有效地针对数据集内的局部和全局关系进行捕捉，AR组件则能充分考虑数据的时间序列特性。同时，通过注意力机制，模型能够优先考虑相关特性，提高预测效果。【结果】研究成果所提出的模型已成功应用于中国西江梧州站的水位预测，在测试集上预测未来3h级别水位的MAE、RMSE和R2分别为 0.086、0.1145和0.9508。【结论】结果表明所提出的CRANet模型在水位预测方面的高可用性、准确度与稳健性，相较于AR、SVR、CNN、LSTM等模型具有更优的MAE、RMSE和R2。

关键词：时间序列水位预测 CNN LSTM attention 影响因素洪水西江

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Enhancement of Visual attention by Color Revealed Using Electroencephalography

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Open Journal of Therapy and Rehabilitation 2024年第1期12卷 1-9页

作者： Moemi Matsuo Takashi Higuchi Takuya Ishibashi Ayano Egashira Toranosuke Abe Hiroya Miyabara Faculty of Rehabilitation Sciences Nishikyushu University Kanzaki Japan Department of Physical Therapy Osaka University of Human Sciences Settsu Japan

attention constitutes a fundamental psychological feature guiding our mental effort toward specific objects, concurrent with processes such as memory, reasoning, and imagination. Visual attention, crucial for selecting surrounding information, often decreases in older adults and patients with cerebrovascular disorders. Effective methods to enhance attention are scarce. Here, we investigated whether color information influences visual attention and brain activity during task performance, utilizing EEG. We examined 13 healthy young adults (seven women and six men;mean age: 21.2 ± 0.58 years) using 19-electrode electroencephalograms to assess the impact of color information on visual attention. The Clinical Assessment for attention cancellation test was conducted under the black, red, and blue color conditions. Wilcoxon’s signed-rank test was used to assess differences in task performance (task time and error) between conditions. Spearman’s rank correlation was utilized to examine the correlation in power levels between task performance and color conditions. Significant variations in total task errors were observed among color conditions. The black condition exhibited the highest error frequency (0.7 ± 0.9 times), followed by the red condition (0.5 ± 0.8 times), with the lowest error frequency occurring in the blue (0.2 ± 0.4 times) condition (black vs. red: P = 0.03;black vs. blue: P = 0.00;red vs. blue: P = 0.032). No time difference was observed. The black condition showed negative delta and high-gamma correlations in the central electrodes. The red condition revealed positive alpha and low-gamma correlations in the frontal and occipital areas. Although no correlations were observed in the blue condition, it enhanced attentional performance. Positive alpha and low-gamma waves might be crucial for spotting attentional errors in key areas. Our findings provide insights into the effects of color information on visual attention and potential neural correlates associated with attentional processes. In conclusion, our

关键词： attention Higher Brain Function Electroencephalography Neuroimaging Rehabilitation

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Detection of a temporal salient object benefits from visual stimulus-specific adaptation in avian midbrain inhibitory nucleus

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Integrative Zoology 2024年第2期19卷 288-306页

作者： Jiangtao WANG Xiaoping RAO Shuman HUANG Zhizhong WANG Xiaoke NIU Minjie ZHU Songwei WANG Li SHI Department of Automation Zhengzhou University School of Electrical EngineeringZhengzhouChina State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics National Center for Magnetic Resonance in WuhanKey Laboratory of Magnetic Resonance in Biological SystemsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhanChina Department of Automation Tsinghua UniversityBeijingChina

Food and predators are the most noteworthy objects for the basic survival of wild animals,and both are often deviant in both spatial and temporal domains and quickly attract an animal’s attention.Although stimulus-specific adaptation(SSA)is considered a potential neural basis of salient sound detection in the temporal domain,related research on visual SSA is limited and its relationship with temporal saliency is uncertain.The avian nucleus isthmi pars magnocellularis(Imc),which is central to midbrain selective attention network,is an ideal site to investigate the neural correlate of visual SSA and detection of a salient object in the time domain.Here,the constant order paradigm was applied to explore the visual SSA in the Imc of pigeons.The results showed that the firing rates of Imc neurons gradually decrease with repetitions of motion in the same direction,but recover when a motion in a deviant direction is presented,implying visual SSA to the direction of a moving object.Furthermore,enhanced response for an object moving in other directions that were not presented ever in the paradigm is also observed.To verify the neural mechanism underlying these phenomena,we introduced a neural computation model involving a recoverable synaptic change with a“center-surround”pattern to reproduce the visual SSA and temporal saliency for the moving object.These results suggest that the Imc produces visual SSA to motion direction,allowing temporal salient object detection,which may facilitate the detection of the sudden appearance of a predator.

关键词： adaptation attention model pigeon saliency

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End-to-End Paired Ambisonic-Binaural Audio Rendering

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 502-513页

作者： Yin Zhu Qiuqiang Kong Junjie Shi Shilei Liu Xuzhou Ye Ju-Chiang Wang Hongming Shan Junping Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Beijing ByteDance Technology Co.Ltd. Shanghai 201102China. Chinese University of Hong Kong Hong KongChina IEEE Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan UniversityShanghai 200433 Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 200031China

Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.

关键词： Ambisonic attention binaural rendering neural network

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Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

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Computers, Materials & Continua 2024年第2期78卷 1995-2022页

作者： Bayi Xu Lei Sun Xiuqing Mao Chengwei Liu Zhiyi Ding School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou450000China Three Academy Information Engineering UniversityZhengzhou450001China The 3rd Research Department Nanjing Research Institute of Electronic EngineeringNanjing210007China

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

关键词： Intrusion detection CNN BiLSTM BiGRU attention

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An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 385-408页

作者： Xianghong Cao Xinyu Wang Xin Geng Donghui Wu Houru An School of Building and Environment Engineering Zhengzhou University of Light IndustryZhengzhou450000China

This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.

关键词： Posture recognition mediapipe BiGRU CNN ELU attention

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